Solutions

Site Selection Sales Forecasting Mobile Analytics Cannibalization and Impact Layers and GIS Map Live Traffic Patterns Competitor Intelligence

Products

Industries

Retail

Find profitable retail locations with AI insights

CRE

Maximize returns across commercial properties

CPG

Optimize product distribution and market coverage

Entertainment

Drive more visitors to entertainment destinations

Banking & Finance

Identify high-potential markets for financial growth

Civic

Support smarter community planning and development decisions

Restaurants

Find high-potential locations for restaurant growth

Brokerage Firms

Identify opportunities and make smarter property decisions

Advertising

Identify high-traffic locations for OOH advertising.

Healthcare

Identify high-potential locations for healthcare growth

Car Washes

Identify high-potential sites, customer demand, and optimize car wash expansion across India.

Automotive

Identify high-potential locations for automotive growth

Fitness and Gyms

Use location data to find the right sites, and expand fitness businesses smarter.

Pet Supplies

Discover the best locations for your pet business.

Wellness

Smarter wellness growth with AI-powered location and consumer insights.

Pricing

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Protect your data’s security, compliance, and availability.

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See Every Street in India Before You Sign the Lease

We turn footfall, spend, demographics and competitor data into one retail analytics software, so site selection, store expansion and growth strategy stop running on gut feel and start running on evidence.

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Indian cities & towns mapped

0M+

Footfall & spend data points

Site-level forecast accuracy

0 wks

Faster expansion decisions

SITE INTELLIGENCE — LIVE VIEWSCORING

Delhi NCR · 8.6

Bengaluru · 9.1

Pune · 7.9

Hyderabad · 8.3

Chennai · 7.6

High-priority siteRecommended launch

Most retail expansion in India still runs on instinct

Rent gets locked in before the data does. Store openings underperform because nobody modelled the catchment, the competition, or the customer. MapZot.AI exists to fix that — with AI location intelligence instead of a gut call.

Blind spot 01

Sites picked before the data is

Teams shortlist locations off broker instinct and footfall they can see with their own eyes — not the footfall, spend and demand a proper retail site selection service would surface.

Blind spot 02

Competitors mapped too late

Without real retail competitor analysis, brands discover a rival's store, cannibalised catchment or category saturation only after signing the lease.

Blind spot 03

Growth plans without a model

Retail growth strategies get built on last year's store performance, not on where the next best 50 customers actually live, work and spend.

One retail analytics platform, six decisions made easier

MapZot.AI brings site selection, competitor mapping, foot traffic data and customer analytics into a single business intelligence layer for retail — built specifically for how India's cities, high streets and malls actually behave.

AI Site Selection

Score every candidate address on footfall, spend power, competition and cannibalisation risk before you shortlist. Purpose-built site selection for retail stores, not generic city-level guesswork.

→ retail site selection services

Foot Traffic & Catchment

Hour-by-hour, street-level retail foot traffic data — pedestrian and vehicular — combined with drive-time and walk-time catchments to size real, addressable demand around a site.

→ retail foot traffic data

Competitor Mapping

See every competing store, category cluster and market gap on one map. Run retail competitor analysis before you commit rent — not after a rival opens 200 metres away.

→ retail competitor analysis

Customer Analytics

Demographic, income and lifestyle profiles for every micro-market, so merchandising and marketing teams work off the same retail customer analytics software as expansion does.

→ retail customer analytics software

Expansion Planning

Whitespace maps, cannibalisation checks and revenue forecasts for your entire network — so retail store expansion becomes a sequenced roadmap, not a city-by-city scramble.

→ retail store expansion

FAQs

Retail location intelligence combines footfall, demographic, spend and competitor data to score how a specific address will perform for a specific retail format. MapZot.AI applies machine learning to millions of these data points across India, turning them into a single explainable score for every candidate site — the core of what AI location intelligence means in practice.

You share your target cities, format and store economics. MapZot ranks candidate locations on footfall, catchment demand, competitor density and rent benchmarks, then forecasts expected revenue and payback for your shortlist — replacing a broker-led search with a data-backed retail site selection service.

Yes. MapZot maps every competing store and category cluster in a market, flags cannibalisation risk against your own network, and highlights genuine white space — so competitor analysis happens before a lease is signed, not after a rival opens nearby.

We combine pedestrian and vehicular movement patterns, mobility signals, and points-of-interest activity, refreshed on an ongoing basis, and layer this with drive-time and walk-time catchments so foot traffic is measured the way a shopper actually experiences a street or mall.

No. Single-city retailers use MapZot to validate their next one or two stores; national chains use it to sequestionuence a hundred-city rollout. The underlying data analytics for retail scales down to one decision or up to an entire network plan.

Generic business intelligence tools visualise data you already have. MapZot is a location intelligence company first — we bring the external data (footfall, competitors, demographics) and the retail-specific models, so the output is a decision — open here, not there — rather than another dashboard to interpret.

Recent Publications

Why Advertising Agencies in India Are Switching to Verified OOH Data — How TraffiRx Makes It Possible

Advertising

Every Impression Matters. But Is It Reaching the Right Audience?

Every media planner has heard the same pitch from an OOH vendor at some point: "This hoarding delivers over a million impressions a week." And every media planner has learned, the hard way, to be skeptical of that number.

For decades, Out-of-Home advertising in India has been sold on Average Daily Traffic (ADT) — a rough estimate of vehicles passing a site, often extrapolated from old traffic studies or, worse, vendor guesswork. Meanwhile, the digital half of an agency's media plan comes with granular dashboards: impressions, reach, frequency, daypart performance, all verifiable in real time.

That gap is exactly why more agencies are asking a simple but uncomfortable question before signing off on OOH budgets: "Can you prove it?"

This is where MapZot.AI's TraffiRx™ platform is changing the conversation.

24th July 2026

Why Advertising Agencies in India Are Switching to Verified OOH Data — How TraffiRx Makes It Possible
Advertising
Every Impression Matters. But Is It Reaching the Right Audience?
Every media planner has heard the same pitch from an OOH vendor at some point: "This hoarding delivers over a million impressions a week." And every media planner has learned, the hard way, to be skeptical of that number.
For decades, Out-of-Home advertising in India has been sold on Average Daily Traffic (ADT) — a rough estimate of vehicles passing a site, often extrapolated from old traffic studies or, worse, vendor guesswork. Meanwhile, the digital half of an agency's media plan comes with granular dashboards: impressions, reach, frequency, daypart performance, all verifiable in real time.
That gap is exactly why more agencies are asking a simple but uncomfortable question before signing off on OOH budgets: "Can you prove it?"
This is where MapZot.AI's TraffiRx™ platform is changing the conversation.
Read more →
24th July 2026

Ready to plan your next store with evidence, not a gut call?

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